Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.
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Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.
日本語の概要は準備中です。原文の説明を表示しています。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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Scan recent git commits for changes that affect user-facing behavior, then draft or update the corresponding documentation pages. Use when docs have fallen behind code changes, after a batch of features lands, or when preparing a release. Trigger keywords - update docs, draft docs, docs from commits, sync docs, catch up docs, doc debt, docs behind, docs drift.
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Inspects sandbox health, traces agent behavior, and diagnoses problems. Use when monitoring a running sandbox, debugging agent issues, or checking sandbox logs. Trigger keywords - monitor nemoclaw sandbox, debug nemoclaw agent issues.
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Use this skill when declaring or manually triggering route-level TanStack Query prefetches with nuxt-ui-tools. Covers page macros, staged dependencies, NuxtLink integration, and error-safe navigation behavior.
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Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
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Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
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Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.
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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
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Adds debugging capabilities and troubleshoots Pine Script issues in TradingView's opaque environment. Use when scripts have errors, unexpected behavior, need debugging tools added, or require troubleshooting. Triggers on "debug", "fix", "error", "not working", "wrong values", or troubleshooting requests.
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Tactical negotiation framework based on Chris Voss's "Never Split the Difference." Use when preparing for negotiations, during live negotiation scenarios, analyzing counterpart behavior, crafting responses to difficult conversations, handling objections, salary/contract negotiations, or when asked about negotiation techniques like mirroring, labeling, calibrated questions, or the Ackerman method.
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Maxim L2 pack. Bundles: All six L1 packs. Primary framework: Behavioral Moat Framing Doctrine (ADR-007).
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Maxim food and beverage video — engineered for craving and order behavior via Sensory Anchoring + Mirror Neurons + ASMR Neural Pathway. Food videos that don't just look appetizing — they trigger physiological buying response.
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Maxim real estate video — engineered for property visit and offer behavior via Spatial Cognition + Endowment Effect + Mental Walk-Through. Real estate video that drives showing-requests and offers, not just views.
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Maxim fashion editorial video — engineered for aspirational desire and status signaling via Aesthetic Resonance + Aspirational Self + Social Comparison Theory. Fashion films that trigger status-driven purchase behavior.
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Maxim L2 pack. Bundles: L1.5 Brand & Design Pro + L1.6 Behavioral Intelligence + L1.2 MemPalace Pro. Primary framework: EAST (Easy, Attractive, Social, Timely).
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Maxim L2 pack. Bundles: L1.1 AI Governance + L1.2 MemPalace Pro + L1.3 Proactive Watch. Primary framework: Fogg Behavior Model.
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Maxim e-commerce direct-response video — engineered for PURCHASE. The primary sales style. Uses AIDA + JTBD + Loss Aversion + Scarcity + Anchoring stacked for maximum conversion. Every second is behavioral leverage.
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Maxim motion-design + kinetic typography video — engineered for comprehension-to-conversion via AIDA + Hick's Law. Explainer-ad hybrid that makes complex value props land in under 15 seconds and converts via behavioral-architected CTA.
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Maxim Presentation Intelligence — pitch decks, investor presentations, executive briefings, product demos, conference talks, narrative-driven visual storytelling, data visualization in slides. Applies Minto Pyramid, Duarte Sparkline, McKinsey Slide Logic, and Dual Coding Theory to every deck. Powered by Maxim behavioral science layer.
日本語の概要は準備中です。原文の説明を表示しています。